A profiler is a program that describes the run time performance of a
program, providing a variety of statistics. This documentation describes the
profiler functionality provided in the modules cProfile, profile
and pstats. This profiler provides deterministic profiling of
Python programs. It also provides a series of report generation tools to allow
users to rapidly examine the results of a profile operation.

The Python standard library provides two different profilers:

cProfile is recommended for most users; it’s a C extension with
reasonable overhead that makes it suitable for profiling long-running
programs. Based on lsprof, contributed by Brett Rosen and Ted
Czotter.

profile, a pure Python module whose interface is imitated by
cProfile. Adds significant overhead to profiled programs. If you’re
trying to extend the profiler in some way, the task might be easier with this
module.

The profile and cProfile modules export the same interface, so
they are mostly interchangeable; cProfile has a much lower overhead but
is newer and might not be available on all systems. cProfile is really a
compatibility layer on top of the internal _lsprof module.

Note

The profiler modules are designed to provide an execution profile for a given
program, not for benchmarking purposes (for that, there is timeit for
reasonably accurate results). This particularly applies to benchmarking
Python code against C code: the profilers introduce overhead for Python code,
but not for C-level functions, and so the C code would seem faster than any
Python one.

This section is provided for users that “don’t want to read the manual.” It
provides a very brief overview, and allows a user to rapidly perform profiling
on an existing application.

To profile an application with a main entry point of foo(), you would add
the following to your module:

importcProfilecProfile.run('foo()')

(Use profile instead of cProfile if the latter is not available on
your system.)

The above action would cause foo() to be run, and a series of informative
lines (the profile) to be printed. The above approach is most useful when
working with the interpreter. If you would like to save the results of a
profile into a file for later examination, you can supply a file name as the
second argument to the run() function:

importcProfilecProfile.run('foo()','fooprof')

The file cProfile.py can also be invoked as a script to profile another
script. For example:

python-mcProfilemyscript.py

cProfile.py accepts two optional arguments on the command line:

cProfile.py[-ooutput_file][-ssort_order]

-s only applies to standard output (-o is not supplied).
Look in the Stats documentation for valid sort values.

When you wish to review the profile, you should use the methods in the
pstats module. Typically you would load the statistics data as follows:

importpstatsp=pstats.Stats('fooprof')

The class Stats (the above code just created an instance of this class)
has a variety of methods for manipulating and printing the data that was just
read into p. When you ran cProfile.run() above, what was printed was
the result of three method calls:

p.strip_dirs().sort_stats(-1).print_stats()

The first method removed the extraneous path from all the module names. The
second method sorted all the entries according to the standard module/line/name
string that is printed. The third method printed out all the statistics. You
might try the following sort calls:

p.sort_stats('name')p.print_stats()

The first call will actually sort the list by function name, and the second call
will print out the statistics. The following are some interesting calls to
experiment with:

p.sort_stats('cumulative').print_stats(10)

This sorts the profile by cumulative time in a function, and then only prints
the ten most significant lines. If you want to understand what algorithms are
taking time, the above line is what you would use.

If you were looking to see what functions were looping a lot, and taking a lot
of time, you would do:

p.sort_stats('time').print_stats(10)

to sort according to time spent within each function, and then print the
statistics for the top ten functions.

You might also try:

p.sort_stats('file').print_stats('__init__')

This will sort all the statistics by file name, and then print out statistics
for only the class init methods (since they are spelled with __init__ in
them). As one final example, you could try:

p.sort_stats('time','cum').print_stats(.5,'init')

This line sorts statistics with a primary key of time, and a secondary key of
cumulative time, and then prints out some of the statistics. To be specific, the
list is first culled down to 50% (re: .5) of its original size, then only
lines containing init are maintained, and that sub-sub-list is printed.

If you wondered what functions called the above functions, you could now (p
is still sorted according to the last criteria) do:

p.print_callers(.5,'init')

and you would get a list of callers for each of the listed functions.

If you want more functionality, you’re going to have to read the manual, or
guess what the following functions do:

p.print_callees()p.add('fooprof')

Invoked as a script, the pstats module is a statistics browser for
reading and examining profile dumps. It has a simple line-oriented interface
(implemented using cmd) and interactive help.

Deterministic profiling is meant to reflect the fact that all function
call, function return, and exception events are monitored, and precise
timings are made for the intervals between these events (during which time the
user’s code is executing). In contrast, statistical profiling (which is
not done by this module) randomly samples the effective instruction pointer, and
deduces where time is being spent. The latter technique traditionally involves
less overhead (as the code does not need to be instrumented), but provides only
relative indications of where time is being spent.

In Python, since there is an interpreter active during execution, the presence
of instrumented code is not required to do deterministic profiling. Python
automatically provides a hook (optional callback) for each event. In
addition, the interpreted nature of Python tends to add so much overhead to
execution, that deterministic profiling tends to only add small processing
overhead in typical applications. The result is that deterministic profiling is
not that expensive, yet provides extensive run time statistics about the
execution of a Python program.

Call count statistics can be used to identify bugs in code (surprising counts),
and to identify possible inline-expansion points (high call counts). Internal
time statistics can be used to identify “hot loops” that should be carefully
optimized. Cumulative time statistics should be used to identify high level
errors in the selection of algorithms. Note that the unusual handling of
cumulative times in this profiler allows statistics for recursive
implementations of algorithms to be directly compared to iterative
implementations.

The primary entry point for the profiler is the global function
profile.run() (resp. cProfile.run()). It is typically used to create
any profile information. The reports are formatted and printed using methods of
the class pstats.Stats. The following is a description of all of these
standard entry points and functions. For a more in-depth view of some of the
code, consider reading the later section on Profiler Extensions, which includes
discussion of how to derive “better” profilers from the classes presented, or
reading the source code for these modules.

This function takes a single argument that can be passed to the exec()
function, and an optional file name. In all cases this routine attempts to
exec() its first argument, and gather profiling statistics from the
execution. If no file name is present, then this function automatically
prints a simple profiling report, sorted by the standard name string
(file/line/function-name) that is presented in each line. The following is a
typical output from such a call:

The first line indicates that 2706 calls were monitored. Of those
calls, 2004 were primitive. We define primitive to
mean that the call was not induced via recursion. The next line:
Orderedby:standardname, indicates that the text string in
the far right column was used to sort the output. The column
headings include:

ncalls

for the number of calls,

tottime

for the total time spent in the given function (and excluding time made in
calls to sub-functions),

percall

is the quotient of tottime divided by ncalls

cumtime

is the total time spent in this and all subfunctions (from invocation till
exit). This figure is accurate even for recursive functions.

percall

is the quotient of cumtime divided by primitive calls

filename:lineno(function)

provides the respective data of each function

When there are two numbers in the first column (for example,
43/3), then the latter is the number of primitive calls, and
the former is the actual number of calls. Note that when the
function does not recurse, these two values are the same, and only
the single figure is printed.

This class constructor creates an instance of a “statistics object”
from a filename (or set of filenames). Stats objects
are manipulated by methods, in order to print useful reports. You
may specify an alternate output stream by giving the keyword
argument, stream.

The file selected by the above constructor must have been created
by the corresponding version of profile or cProfile.
To be specific, there is no file compatibility guaranteed with
future versions of this profiler, and there is no compatibility
with files produced by other profilers. If several files are
provided, all the statistics for identical functions will be
coalesced, so that an overall view of several processes can be
considered in a single report. If additional files need to be
combined with data in an existing Stats object, the
add() method can be used.

This method for the Stats class removes all leading path
information from file names. It is very useful in reducing the
size of the printout to fit within (close to) 80 columns. This
method modifies the object, and the stripped information is lost.
After performing a strip operation, the object is considered to
have its entries in a “random” order, as it was just after object
initialization and loading. If strip_dirs() causes two
function names to be indistinguishable (they are on the same line
of the same filename, and have the same function name), then the
statistics for these two entries are accumulated into a single
entry.

This method of the Stats class accumulates additional profiling
information into the current profiling object. Its arguments should refer to
filenames created by the corresponding version of profile.run() or
cProfile.run(). Statistics for identically named (re: file, line, name)
functions are automatically accumulated into single function statistics.

Save the data loaded into the Stats object to a file named
filename. The file is created if it does not exist, and is
overwritten if it already exists. This is equivalent to the method
of the same name on the profile.Profile and
cProfile.Profile classes.

This method modifies the Stats object by sorting it
according to the supplied criteria. The argument is typically a
string identifying the basis of a sort (example: 'time' or
'name').

When more than one key is provided, then additional keys are used
as secondary criteria when there is equality in all keys selected
before them. For example, sort_stats('name','file') will sort
all the entries according to their function name, and resolve all
ties (identical function names) by sorting by file name.

Abbreviations can be used for any key names, as long as the abbreviation is
unambiguous. The following are the keys currently defined:

Valid Arg

Meaning

'calls'

call count

'cumulative'

cumulative time

'cumtime'

cumulative time

'file'

file name

'filename'

file name

'module'

file name

'ncalls'

call count

'pcalls'

primitive call count

'line'

line number

'name'

function name

'nfl'

name/file/line

'stdname'

standard name

'time'

internal time

'tottime'

internal time

Note that all sorts on statistics are in descending order (placing
most time consuming items first), where as name, file, and line
number searches are in ascending order (alphabetical). The subtle
distinction between 'nfl' and 'stdname' is that the
standard name is a sort of the name as printed, which means that
the embedded line numbers get compared in an odd way. For example,
lines 3, 20, and 40 would (if the file names were the same) appear
in the string order 20, 3 and 40. In contrast, 'nfl' does a
numeric compare of the line numbers. In fact,
sort_stats('nfl') is the same as sort_stats('name','file','line').

For backward-compatibility reasons, the numeric arguments -1,
0, 1, and 2 are permitted. They are interpreted as
'stdname', 'calls', 'time', and 'cumulative'
respectively. If this old style format (numeric) is used, only one
sort key (the numeric key) will be used, and additional arguments
will be silently ignored.

The arguments provided (if any) can be used to limit the list down
to the significant entries. Initially, the list is taken to be the
complete set of profiled functions. Each restriction is either an
integer (to select a count of lines), or a decimal fraction between
0.0 and 1.0 inclusive (to select a percentage of lines), or a
regular expression (to pattern match the standard name that is
printed; as of Python 1.5b1, this uses the Perl-style regular
expression syntax defined by the re module). If several
restrictions are provided, then they are applied sequentially. For
example:

print_stats(.1,'foo:')

would first limit the printing to first 10% of list, and then only print
functions that were part of filename .*foo:. In contrast, the
command:

print_stats('foo:',.1)

would limit the list to all functions having file names .*foo:, and
then proceed to only print the first 10% of them.

This method for the Stats class prints a list of all functions that
called each function in the profiled database. The ordering is identical to
that provided by print_stats(), and the definition of the restricting
argument is also identical. Each caller is reported on its own line. The
format differs slightly depending on the profiler that produced the stats:

With profile, a number is shown in parentheses after each caller to
show how many times this specific call was made. For convenience, a second
non-parenthesized number repeats the cumulative time spent in the function
at the right.

With cProfile, each caller is preceded by three numbers:
the number of times this specific call was made, and the total
and cumulative times spent in the current function while it was
invoked by this specific caller.

This method for the Stats class prints a list of all
function that were called by the indicated function. Aside from
this reversal of direction of calls (re: called vs was called by),
the arguments and ordering are identical to the
print_callers() method.

One limitation has to do with accuracy of timing information. There is a
fundamental problem with deterministic profilers involving accuracy. The most
obvious restriction is that the underlying “clock” is only ticking at a rate
(typically) of about .001 seconds. Hence no measurements will be more accurate
than the underlying clock. If enough measurements are taken, then the “error”
will tend to average out. Unfortunately, removing this first error induces a
second source of error.

The second problem is that it “takes a while” from when an event is dispatched
until the profiler’s call to get the time actually gets the state of the
clock. Similarly, there is a certain lag when exiting the profiler event
handler from the time that the clock’s value was obtained (and then squirreled
away), until the user’s code is once again executing. As a result, functions
that are called many times, or call many functions, will typically accumulate
this error. The error that accumulates in this fashion is typically less than
the accuracy of the clock (less than one clock tick), but it can accumulate
and become very significant.

The problem is more important with profile than with the lower-overhead
cProfile. For this reason, profile provides a means of
calibrating itself for a given platform so that this error can be
probabilistically (on the average) removed. After the profiler is calibrated, it
will be more accurate (in a least square sense), but it will sometimes produce
negative numbers (when call counts are exceptionally low, and the gods of
probability work against you :-). ) Do not be alarmed by negative numbers in
the profile. They should only appear if you have calibrated your profiler,
and the results are actually better than without calibration.

The profiler of the profile module subtracts a constant from each event
handling time to compensate for the overhead of calling the time function, and
socking away the results. By default, the constant is 0. The following
procedure can be used to obtain a better constant for a given platform (see
discussion in section Limitations above).

The method executes the number of Python calls given by the argument, directly
and again under the profiler, measuring the time for both. It then computes the
hidden overhead per profiler event, and returns that as a float. For example,
on an 800 MHz Pentium running Windows 2000, and using Python’s time.clock() as
the timer, the magical number is about 12.5e-6.

The object of this exercise is to get a fairly consistent result. If your
computer is very fast, or your timer function has poor resolution, you might
have to pass 100000, or even 1000000, to get consistent results.

When you have a consistent answer, there are three ways you can use it:

The Profile class of both modules, profile and cProfile,
were written so that derived classes could be developed to extend the profiler.
The details are not described here, as doing this successfully requires an
expert understanding of how the Profile class works internally. Study
the source code of the module carefully if you want to pursue this.

If all you want to do is change how current time is determined (for example, to
force use of wall-clock time or elapsed process time), pass the timing function
you want to the Profile class constructor:

pr=profile.Profile(your_time_func)

The resulting profiler will then call your_time_func().

profile.Profile

your_time_func() should return a single number, or a list of
numbers whose sum is the current time (like what os.times()
returns). If the function returns a single time number, or the
list of returned numbers has length 2, then you will get an
especially fast version of the dispatch routine.

Be warned that you should calibrate the profiler class for the
timer function that you choose. For most machines, a timer that
returns a lone integer value will provide the best results in terms
of low overhead during profiling. (os.times() is pretty
bad, as it returns a tuple of floating point values). If you want
to substitute a better timer in the cleanest fashion, derive a
class and hardwire a replacement dispatch method that best handles
your timer call, along with the appropriate calibration constant.

cProfile.Profile

your_time_func() should return a single number. If it
returns integers, you can also invoke the class constructor with a
second argument specifying the real duration of one unit of time.
For example, if your_integer_time_func() returns times
measured in thousands of seconds, you would construct the
Profile instance as follows:

pr=profile.Profile(your_integer_time_func,0.001)

As the cProfile.Profile class cannot be calibrated, custom
timer functions should be used with care and should be as fast as
possible. For the best results with a custom timer, it might be
necessary to hard-code it in the C source of the internal
_lsprof module.